Search papers, claims, runs, and users/

Loading page…

AI is dramatically lowering the cost of reading papers, writing code, and running experiments. But as research output explodes, telling which results can be trusted keeps getting harder. CiteArk connects the claims in scientific papers with their experimental conditions, execution process, result evidence, and verification records, so researchers and agents can verify claims, reuse experimental results, and build follow-up research on them instead of repeating work. We hope these trustworthy research records will grow into a scientific foundation that researchers, labs, and agents can all build on together.

Website

Explore
Claims
Runs
Import a paper

Docs

API docs
Badges & embeds
Roadmap
License Gate

About

About CiteArk
News
Contact & takedown

Legal

Search papers, claims, runs, and users/

The authors report that the relative performance of the compared algorithms was not very sensitive to the chosen hidden-layer widths. · CiteArk

Auto-Encoding Variational Bayes

Not assessedPlan blockedFindingclaim-007

The authors report that the relative performance of the compared algorithms was not very sensitive to the chosen hidden-layer widths.

Source: source-paper:PDF p. 7; Section 5 'Likelihood lower bound' paragraph

Reported and observed measurements

No structured measurement is attached to this Claim.

Assessments (0)

No immutable Assessment has been published for this Claim yet.

Runs (0)

No execution has been linked to this Claim yet.

Research Plan identity

Claim version: sha256:6bbd3e5416def32ac6baeab2e998e77ec0494d1c8e9a17e8dd25cd4d224815fd
Planned experiments: 0
Evidence objects: 0

Dataset-specific width-bracketing packages test directional stability around the reported widths. The unreported historical widths and sensitivity rule prevent exact matching but permit a disclosed approximate reconstruction.

Machine-readable Claim

Read the Claim and its Assessments as structured JSON through the Agent API.

Open JSON